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Alex Martelli - Exception and error handling in Python 2 and Python 3

Alex Martelli - Exception and error handling in Python 2 and Python 3

Handling errors and exceptions optimally is crucial in solid Python programs. Some technical details have changed in Python 3, and the talk covers those, but the core of "best practices" is quite enduring, and the talk focuses on presenting and explaining them.


PyCon 2016

May 29, 2016

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  1. ©2016 Google -- [email protected] Exceptions & error handling in Python

    2 and Python 3 http://www.aleax.it/pycon16.pdf
  2. Python in a Nutshell 3rd ed This talk cover parts

    of Chapter 5 of the Early Release e-book version 50% off: http:/ /www.oreilly.com/go/python45 disc.code TS2016; 40% on paper book pre-order 2 DRM-free e-book epub, mobi, PDF… copy it to all of your devices! Send us feedback while we can still do major changes!
  3. Exceptions: not always errors >>> 1/0 Traceback (most recent call

    last): File "<stdin>", line 1, in <module> ZeroDivisionError: division by zero >>> next(iter([])) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration >>> Throughout, I use exc to mean exception 3
  4. The try statement try: ...block... [except (type, type...) [as x]:]

    ... (0+ except clauses, narrowest first) [else:] ... (optional: execute without exception guard iff no exc in block; must have 1+ except) [finally:] ... (optional: execute unconditionally at end; no break, return [continue forbidden]) 4
  5. The raise statement raise exception_object must be an instance of

    BaseException (in v2, could be a subclass -- avoid that!) raise must be in an except clause (or a function called, directly or not, from one) re-raises the exception being handled 5
  6. When to raise and why def cross_product(seq1, seq2): if not

    seq1 or not seq2: raise ValueError('empty seq arg') return [(x1, x2) for x1 in seq1 for x2 in seq2] Note: no duplicate checks of errors that Python itself checks anyway, e.g seq1 or seq2 not being iterable (that will presumably give a TypeError, which is probably fine). 6
  7. Exceptions wrapping (v3) v3 only (upgrade to v3, already!-) traceback

    is held by the exception object exc.with_traceback(tb) gives a copy of exc with a different traceback last exc caught is __context__ of new one raise new_one from x sets __cause__ to x, which is None or exception instance 7
  8. >>> def inverse(x): ... try: return 1/x ... except ZeroDivisionError

    as err: ... raise ValueError() from err >>> inverse(0) Traceback (most recent call last): File "<stdin>", line 2, in inverse ZeroDivisionError: division by zero The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 4, in inverse ValueError >>> try: print('inverse is', inverse(0)) ... except ValueError: print('no inverse there') no inverse there 8
  9. exc __context__ in v3 try: 1/0 except ZeroDivisionError: 1+'x' Traceback

    (most recent call last): File "<stdin>", line 1, in <module> ZeroDivisionError: division by zero During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 3, in <module> TypeError: unsupported operand type(s) for +: 'int' and 'str' 9 v2 would hide 1st exc; v3 shows both!
  10. the with statement with x [as y]: ...block... ...is roughly

    the same as...: y = x.__enter__() _ok = True try: ...block... except: _ok = False if not x.__exit__(*sys.exc_info()): raise finally: if _ok: x.__exit__(None, None, None) 10
  11. Making a context manager @contextlib.contextmanager def a_context_manager(args_if_any): # __init__ code

    if any try: # __enter__ code yield some_result except (handled, types) as exc: # __exit__ w/exception # maybe `raise` again if needed finally: # unconditional __exit__ code 11
  12. Exceptions in generators caller may use generator.throw(exc) like raise exc

    at generator's yield in v2 may pass type, value, traceback typically used as...: try: result = yield previous except GeneratorExit: clean-up caller may use generator.close() which is just like: generator.throw(GeneratorExit()) 12
  13. exception propagation exceptions "bubble up" through the stack of call

    frames (callee to caller to ...) until caught in an except clause or __exit__ in a context manager (as finally clauses or context managers are "bubbled through", they execute) if never caught, all the way to the top where sys.excepthook may act ...but essentially just reporting/logging lastly, atexit-registered functions 13
  14. exceptions hierarchy (v2) BaseException Exception StandardError ...many types/subtypes... EnvironmentError IOError,

    OSError StopIteration Warning GeneratorExit KeyboardInterrupt SystemExit 14
  15. exceptions hierarchy (v3) BaseException Exception ...many types/subtypes... OSError (AKA: IOError,

    EnvironmentError) ...subtypes, e.g FileNotFoundError... StopIteration Warning GeneratorExit KeyboardInterrupt SystemExit 15
  16. OSError subclasses (v3) def read_or_def(path, default): try: with open(path) as

    f: return f.read() except IOError as e: if e.errno == errno.ENOENT: return default raise def read_or_def(path, default): try: with open(path) as f:return f.read() except FileNotFoundError: return default 16
  17. custom exceptions best practice: have your module or package define

    a custom exc class: class Error(Exception): "docstring" this lets client code easily catch errors specific to your module or package also use multiple inheritance to define your module's versions of standard exceptions: class MyTE(Error, TypeError): "doc" this also lets client code easily catch (e.g) TypeErrors wherever they come from 17
  18. Strategies: LBYL, EAFP Look Before You Leap: check that all

    preconditions are met if not, raise exc (or otherwise give error) if met, perform op (no exc expected) Easier to Ask Forgiveness than Permission just try performing the operation Python catches any prereq violations and raises exc on your behalf optionally catch to transform/enrich 18
  19. LBYL problems duplicates work Python performs anyway to check preconditions

    obscures code clarity due to structure: check, raise if it fails ...(repeat N times)… actual useful work (only at the end) some checks might erroneously be omitted resulting in unexpected exceptions things (e.g filesystem) may change at any time (inc. between checks and operation!) 19
  20. LBYL vs EAFP 20 def read_or_default(path, default): # spot the

    many problems...: if os.path.exists(path): with open(path) as f: return f.read() else: return default def read_or_default(path, default): try: with open(path) as f: return f.read() except FileNotFoundError: return default
  21. how to EAFP right def trycall(obj, attr, default, *a, **k):

    try: return getattr(obj, attr)(*a, **k) except AttributeError: return default def trycall(obj, attr, default, *a, **k): try: method = getattr(obj, attr) except AttributeError: return default else: return method(*a, **k) 21 Keep it narrow: DON'T guard too many operations within a try clause!
  22. Errors in large programs consider all possible causes of errors...:

    bugs in your code, or libraries you use cover those with unit tests mismatches between libraries' prereqs and your understanding of them cover those with integration tests invalid inputs to your code great use for try/except! remember: let Python do most checks! invalid environment/config: ditto 22
  23. Case-by-case handling the info you (the coder) need (to fix

    bugs etc) is NOT the same the user needs (to remedy invalid inputs/environment/etc) think of the user: everything else follows never show the user a traceback they can't do anything with it! archive it, send to yourself (w/perm!), ... design user error messages with care focus on what they can/should do NOW! if feasible, restart the program (maybe with snapshot/restore; ideally with a "watchdog") 23
  24. logging Python stdlib's logging package can be very rich and

    complex if used to the fullest worth your effort! logs is how you debug (and optimize, etc) esp. server programs ensure your logs are machine-parsable, design log-parsing scripts carefully when in doubt, default to logging all info program state, environment, inputs, ... don't log just errors: logging.info can give you precious "base-case" comparisons too 24
  25. avoid assert although assert seems an attractive way to check

    inputs and environment... ...it's NOT: it's an "attractive nuisance"! becomes no-op when you run optimized but inputs &c can be wrong even then! often duplicates checks Python performs it's usually a sub-case of LBYL... use ONLY for sanity checks on internal state (and as "executable docs") while you're developing and debugging your program! 25